Recent work in deep learning has led to impressive progress in the area of generative modeling [1]}, [2]}, and in particular, score-based generative models [3]}. Given i.i.d. training samples drawn from a high-dimensional probability density \(p\) , score-based generative models can estimate the score of \(p\) , defined as \(\nabla _x \log p(x)\) , by training a NN via denoising score matching [3]}. These models can produce new samples from \(p\)  by initializing \(x_0 \sim {\cal N}(0,I_N)\) , and running annealed Langevin dynamics for \(T\)  steps:
\(x_{t+1} = x_t + \eta _t \nabla \log p(x_t) + z_t,\) 
